CN113155776A - Prediction method for optimal harvest time of oranges - Google Patents
Prediction method for optimal harvest time of oranges Download PDFInfo
- Publication number
- CN113155776A CN113155776A CN202110470267.7A CN202110470267A CN113155776A CN 113155776 A CN113155776 A CN 113155776A CN 202110470267 A CN202110470267 A CN 202110470267A CN 113155776 A CN113155776 A CN 113155776A
- Authority
- CN
- China
- Prior art keywords
- citrus
- near infrared
- optimal
- infrared spectrum
- oranges
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003306 harvesting Methods 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 18
- 235000020971 citrus fruits Nutrition 0.000 claims abstract description 68
- 241000207199 Citrus Species 0.000 claims abstract description 62
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 33
- 238000001228 spectrum Methods 0.000 claims abstract description 28
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 7
- 238000012216 screening Methods 0.000 claims abstract description 5
- 238000009614 chemical analysis method Methods 0.000 claims abstract description 4
- 238000004940 physical analysis method Methods 0.000 claims abstract description 4
- 238000012545 processing Methods 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000002835 absorbance Methods 0.000 claims description 5
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 230000004927 fusion Effects 0.000 abstract 1
- 239000000523 sample Substances 0.000 description 21
- 235000013399 edible fruits Nutrition 0.000 description 17
- 239000011159 matrix material Substances 0.000 description 5
- 239000002420 orchard Substances 0.000 description 4
- 239000002253 acid Substances 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 241000675108 Citrus tangerina Species 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000009659 non-destructive testing Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 210000001015 abdomen Anatomy 0.000 description 1
- 230000003187 abdominal effect Effects 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
- 235000019634 flavors Nutrition 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012625 in-situ measurement Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000000985 reflectance spectrum Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 239000011973 solid acid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0098—Plants or trees
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Botany (AREA)
- Wood Science & Technology (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a method for predicting an optimal harvesting period of oranges, which comprises the following steps of (1) collecting accumulated temperature data aiming at orange samples to obtain an optimal harvesting standard based on the accumulated temperature; collecting near infrared spectrum data of a citrus sample, and finishing spectrum data processing and characteristic screening; obtaining the internal quality index of the oranges by a physical and chemical analysis method; (2) determining an optimal harvesting standard based on the near infrared spectrum by combining the characteristics of the near infrared spectrum and the internal quality indexes of the oranges; (3) fusing an optimal harvesting standard based on accumulated temperature and an optimal harvesting standard based on near infrared spectrum, and establishing an optimal citrus harvesting period prediction model through an artificial intelligence algorithm; (4) measuring short wave near infrared spectrum, temperature and position information of the citrus sample to be measured, inputting the short wave near infrared spectrum, temperature and position information into a citrus optimal harvesting period prediction model, and quantitatively calculating the optimal harvesting period of the citrus; (5) obtaining the predicted result of the harvest time of the citrus sample in a certain place. The invention adopts a multi-information fusion mode, and can accurately predict the harvest time of the oranges.
Description
Technical Field
The invention belongs to the technical field of fruit nondestructive testing, and particularly relates to a prediction method of an optimal harvest time of oranges.
Background
The maturity is an important index for evaluating the fruit quality and is also a main basis for predicting the harvesting period. The harvesting has great influence on the storage effect of the oranges in due time, the harvesting time is early, the fruits are not fully developed, the size is small, the sugar accumulation is insufficient, and the mouthfeel is poor; too late harvesting time and over-ripening of fruits lead to soft surface, insufficient hardness, poor storage resistance and poor taste. The national standard requires that the ripeness of the citrus fruits is suitable for eating. However, this degree of appropriateness is difficult to grasp, and there are large individual differences in the perception of fruit taste by different examiners. According to the definition of GB/T12947-2008 fresh citrus, the maturity means that the fruit develops to a proper maturity level for eating. The fruits are picked at proper maturity, the mature condition is consistent with the market requirement (green area of the fruits is allowed in the early picking stage, oranges are less than or equal to 1/3, wide-skinned citrus is less than or equal to 1/2, early-maturing varieties are less than or equal to 7/10), and the green removing treatment is allowed when necessary; picking reasonably, and keeping the fruits intact and fresh; cleaning fruit surfaces; the flavor is normal. It is shown that the maturity is only qualitatively specified in the standard.
China is a large producing country of citrus fruits, but a sales channel chain is imperfect, citrus is not easy to store and is easily influenced by climate, the harvesting time depends on experience, and most citrus fruits on the market judge the internal quality of the fruits by manually screening the sizes and the colors of the fruits of the same variety. The ripeness of the citrus fruits is different, the overall quality is seriously influenced, and the market competitiveness of the citrus fruits is reduced. With the rapid development of economy and society and the improvement of living standard, the taste, shape and nutrition of fruits are more and more concerned by the general public. Therefore, the best harvesting time of the citrus fruits is known, the maturity is judged, the storage time is prolonged, the phenomenon of fruit late sale can be well improved, harvesting is carried out in the best harvesting time, and the maximum profit is achieved.
Disclosure of Invention
The invention aims to provide a method for predicting the optimal harvesting time of oranges, which can more accurately predict the optimal harvesting time of the oranges.
The technical scheme of the invention is as follows: the method comprises the following steps:
(1) collecting accumulated temperature data aiming at a citrus sample to obtain an optimal collection standard based on the accumulated temperature;
the specific scheme is as follows: analyzing the difference of effective accumulated temperature of different geographical positions of the orchard garden, and optimizing the network node layout of the temperature sensor; analyzing the correlation between the effective accumulated temperature and the internal quality index of the citrus, and determining the effective accumulated temperature threshold value required by the maturity of regional citrus; and establishing an effective accumulated temperature prediction model driven by real-time and historical temperature data, and predicting the optimal recovery period by using the information of the temperature sensor.
(2) Collecting near infrared spectrum data of a citrus sample, and finishing spectrum data processing and characteristic screening; the near infrared spectrum data are processed by adopting smoothing, denoising, standardization, centralization, multivariate scattering correction and standard normal variable spectrum preprocessing methods to obtain the response characteristic of a specific near infrared spectrum waveband of the citrus. Obtaining the internal quality index of the oranges by a physical and chemical analysis method;
(3) determining an optimal harvesting standard based on the near infrared spectrum by combining the characteristics of the near infrared spectrum and the internal quality indexes of the oranges; fusing an optimal harvesting standard based on accumulated temperature and an optimal harvesting standard based on near infrared spectrum, and establishing an optimal citrus harvesting period prediction model through an artificial intelligence algorithm;
(4) measuring short wave near infrared spectrum, temperature and position information of the citrus sample to be measured, inputting the short wave near infrared spectrum, temperature and position information into the citrus optimal harvesting period prediction model, and carrying out quantitative calculation on the optimal harvesting period of the citrus;
(5) obtaining the predicted result of the harvest time of the citrus sample in a certain place.
Furthermore, when the near infrared spectrum datA of the citrus sample is collected, the absorbance calculation formulA is corrected to be log (S-A-D)/(R-D), wherein A represents an ambient light spectrum, S represents A citrus sample diffuse reflection spectrum, R represents A reference spectrum, and D represents A dark current spectrum.
Further, one or more of citrus internal quality indexes of sugar degree, SSC, TA, surface color, VC and SSC/TA.
Further, the artificial intelligence algorithm includes, but is not limited to, one or more of a neural network, a support vector machine.
Compared with the prior art, the invention has the following beneficial effects: (1) the method can comprehensively utilize the near infrared spectrum and the position information of the oranges and the temperature data of the temperature sensor to complement information from the internal quality and the development accumulated temperature of the oranges and tangerines, and realizes the prediction of the accurate harvest time of the oranges and tangerines.
(2) The invention has the advantages of strong timeliness, high efficiency, good result consistency and strong automation degree by a nondestructive testing mode, and can accurately predict the harvest time of the oranges.
Drawings
FIG. 1 is a flow chart of a method for predicting the optimal harvest time of citrus.
The noun explains: SSC: soluble solid, soluble solid content;
TA: titratable acid, titratable acid;
SSC/TA: i.e. the ratio of soluble solid to titratable acid, referred to as the solid-acid ratio.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1, a method for predicting the optimal harvest time of citrus comprises the following steps:
(1) collecting accumulated temperature data aiming at a citrus sample to obtain an optimal collection standard based on the accumulated temperature;
(2) collecting near infrared spectrum data of a citrus sample, and finishing spectrum data processing and characteristic screening; the near infrared spectrum data are processed by adopting smoothing, denoising, standardization, centralization, multivariate scattering correction and standard normal variable spectrum preprocessing methods to obtain the response characteristic of a specific near infrared spectrum waveband of the citrus. Obtaining the internal quality index of the oranges by a physical and chemical analysis method;
(3) determining an optimal harvesting standard based on the near infrared spectrum by combining the characteristics of the near infrared spectrum and the internal quality indexes of the oranges; fusing an optimal harvesting standard based on accumulated temperature and an optimal harvesting standard based on near infrared spectrum, and establishing an optimal citrus harvesting period prediction model through an artificial intelligence algorithm;
(4) measuring short wave near infrared spectrum, temperature and position information of the citrus sample to be measured, inputting the short wave near infrared spectrum, temperature and position information into the citrus optimal harvesting period prediction model, and carrying out quantitative calculation on the optimal harvesting period of the citrus;
(5) obtaining the predicted result of the harvest time of the citrus sample in a certain place.
Preferably, when near infrared spectral datA of A citrus sample is collected, the absorbance calculation formulA is modified to-log (S-A-D)/(R-D), where A represents the ambient light spectrum, S represents the citrus sample diffuse reflectance spectrum, R represents the reference spectrum, and D represents the dark current spectrum.
Preferably, one or more of citrus internal quality indexes of sugar degree, SSC, TA, surface color, VC and SSC/TA.
Preferably, the artificial intelligence algorithm includes, but is not limited to, one or more of a neural network, a support vector machine.
Citrus is a water-based organism with water content around 80%, and changes in ambient temperature can result in changes in citrus sample temperature, such as different temperatures for the same sample in the morning and in the afternoon, and different temperatures for citrus samples in and outside the corona. The temperature rise can cause the near infrared characteristic wavelength corresponding to the H-containing group to shift towards the short wave direction, so that the temperature is an important factor for the near infrared nondestructive detection of the citrus on the tree. Near infrared spectra were taken at various temperatures from 10-40 ℃ at 5 ℃ intervals using a batch of samples. And (3) inserting a probe type infrared temperature sensor near a citrus spectrum scanning point while collecting the spectrum, recording the temperature of the citrus sample, and analyzing the rule of the near infrared spectrum changing along with the temperature.
Under two different temperatures, the same orange can be approximately considered to have no change in physical and chemical properties except the temperature, so that the difference spectrum of the same orange under different temperatures is only related to the temperature. Pure temperature difference spectrum matrix, see formula (1)
Wherein i is the citrus sample number, e.g., 30 samples used to produce a pure temperature difference spectrum matrix, i =1 … 30; j is the temperature gradient of the ith citrus sample, j =1, 2, …, n, with an interval of 5oC, in this case n = 7; d is a difference spectrum matrix, and Xij is a spectrum corresponding to the ith sample temperature gradient j.
The spectrum X of the citrus can be considered to consist of the measured target component (XP), the external disturbance (XQ) and the residual error (R), as in equation (2). Where P and Q are the load vectors of the spectral projection onto the target component (e.g., SSC) and the disturbance (e.g., temperature), respectively. In other words, X can be divided into useful fractions X × = XP and interferences X + = XQ; if the disturbance X + can be estimated, X may be calculated. In the project, the pure temperature spectrum matrix (D) is constructed by designing the scheme, and the pure target matrix X is calculated by adopting chemometrics methods such as EPO, GLSM, Repeatability file and the like, so that the prediction capability of the orange optimal harvest period prediction model on variable-temperature orange samples is improved.
Ambient light is another major disturbing factor for in-situ measurements in the orchard site. Sunlight changes due to time (from early to late) and space (inside and outside a canopy), and the sunlight is easily superposed with an active light source of an instrument on citrus in a short-wave near-infrared frequency band, so that the measurement result is higher. In general near infrared detection, diffuse reflectance (S), reference (R) and dark current (D) spectra of citrus samples are measured for each sample and the absorbance spectrum-log (S-D)/(R-D) is calculated. And (3) providing an ambient light interference deduction scheme, namely acquiring an ambient light spectrum (A) during near-infrared detection of the citrus sample, and correcting an absorbance calculation formulA into-log (S-A-D)/(R-D).
The fruit trees have differences in individual life due to the terrain, soil, illumination, moisture and other field conditions, so scientific and reasonable sampling is very key. In the plot, a number of fruit trees are selected to represent the population characteristics of the fruit trees, the number of which is determined with reference to formula (1). If the SD value is larger, the sampling number is increased; if the SD value is small, the number of samples is reduced.
Wherein y is a statistical contribution with 5% confidence, typically taken to be 1.96; SD is standard deviation between trees; ε is the sampling precision and is usually 0.5.
The effective accumulated temperature is the sum of effective temperatures in the growth period of the oranges and can represent the heat required by the growth of the oranges. However, most of China is in hilly and mountainous orchards, and the temperatures of mountain tops, mountain bottoms and abdominal regions are different. Carrying out effective accumulated temperature measurement and calculation research by adopting an experimental method, and monitoring data by using a sensor for real-time data; data after the time node is observed does not occur, cannot be acquired from the sensor, and is replaced by the average value of the local near-ten-year agricultural meteorological parallel observation data. And wireless sensor network nodes are arranged at the top, bottom and abdomen areas of the mountain, so that the temperature from flowering to harvesting is monitored in real time, and the effective accumulated temperature is calculated. By analyzing the difference of effective accumulated temperature of different geographic positions of the orchard, data support is provided for optimizing the node layout of the temperature sensor network. Analyzing the correlation between the effective accumulated temperature and internal quality indexes such as citrus sugar degree, SSC, TA, surface color, VC, SSC/TA and the like, and determining an effective accumulated temperature threshold value corresponding to citrus suitable for harvesting as a threshold value for the optimal harvesting period prediction based on the temperature sensor information; and establishing an effective accumulated temperature estimation model based on the real-time monitoring data and the historical temperature data of the temperature sensor, and predicting the optimal harvesting period by applying the effective accumulated temperature.
In principle, the short wave near infrared technology can quickly, nondestructively and accurately measure the main physical and chemical property indexes of the oranges in situ, but is difficult to be suitable for the whole growth cycle. The effective temperature is easily affected by abnormal climate, and the result error is larger, but the method is suitable for the whole growth cycle, and can be just used as an auxiliary means to form good complementation with near infrared. Meanwhile, the position information acquired by the GPS is matched with the short-wave near-infrared prediction results one by one to generate a harvest decision prescription chart taking the tree as a unit, so that the aim of accurate harvest can be fulfilled.
Although embodiments of the present invention have been described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A prediction method of an optimal harvest time of oranges is characterized by comprising the following steps: the method comprises the following steps:
(1) collecting accumulated temperature data aiming at a citrus sample to obtain an optimal collection standard based on the accumulated temperature;
collecting near infrared spectrum data of a citrus sample, and finishing spectrum data processing and characteristic screening; obtaining the internal quality index of the oranges by a physical and chemical analysis method;
(2) determining an optimal harvesting standard based on the near infrared spectrum by combining the characteristics of the near infrared spectrum and the internal quality indexes of the oranges;
(3) fusing an optimal harvesting standard based on accumulated temperature and an optimal harvesting standard based on near infrared spectrum, and establishing an optimal citrus harvesting period prediction model through an artificial intelligence algorithm;
(4) measuring short wave near infrared spectrum, temperature and position information of the citrus sample to be measured, inputting the short wave near infrared spectrum, temperature and position information into the citrus optimal harvesting period prediction model, and carrying out quantitative calculation on the optimal harvesting period of the citrus;
(5) obtaining the predicted result of the harvest time of the citrus sample in a certain place.
2. The method for predicting the optimal harvesting time of citrus according to claim 1, wherein: when near infrared spectrum datA of A citrus sample is collected, an absorbance calculation formulA is corrected to be-log (S-A-D)/(R-D), wherein A represents an ambient light spectrum, S represents A citrus sample diffuse reflection spectrum, R represents A reference spectrum, and D represents A dark current spectrum.
3. The method for predicting the optimal harvesting time of citrus according to claim 1, wherein: the citrus internal quality index comprises one or more of sugar degree, SSC, TA, surface color, VC and SSC/TA.
4. The method for predicting the optimal harvesting time of citrus according to claim 1, wherein: the artificial intelligence algorithm comprises one or more of a neural network and a support vector machine.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110470267.7A CN113155776A (en) | 2021-04-29 | 2021-04-29 | Prediction method for optimal harvest time of oranges |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110470267.7A CN113155776A (en) | 2021-04-29 | 2021-04-29 | Prediction method for optimal harvest time of oranges |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113155776A true CN113155776A (en) | 2021-07-23 |
Family
ID=76872393
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110470267.7A Pending CN113155776A (en) | 2021-04-29 | 2021-04-29 | Prediction method for optimal harvest time of oranges |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113155776A (en) |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030129579A1 (en) * | 2001-09-04 | 2003-07-10 | Bornhop Darryl J. | Multi-use multimodal imaging chelates |
| CN1635202A (en) * | 2003-12-30 | 2005-07-06 | 袁兵 | Nano thermal storage warming all band infrared fibrous composition |
| EP1682566A2 (en) * | 2003-11-12 | 2006-07-26 | E.I. Dupont De Nemours And Company | Delta-15 desaturases suitable for altering levels of polyunsaturated fatty acids in oleaginous plants and yeast |
| JP2010025883A (en) * | 2008-07-24 | 2010-02-04 | Gifu Univ | Method of evaluating freshness of fruit and vegetable |
| CN103278473A (en) * | 2013-05-14 | 2013-09-04 | 中国热带农业科学院分析测试中心 | Method for determining piperine and moisture content in white pepper and evaluating quality of white pepper |
| WO2013148249A1 (en) * | 2012-03-27 | 2013-10-03 | Genentech, Inc. | Improved harvest operations for recombinant proteins |
| CN103971176A (en) * | 2014-05-07 | 2014-08-06 | 中国农业科学院柑桔研究所 | Method and system for optimizing harvesting decision of citrus fruits |
| CN104316489A (en) * | 2014-08-18 | 2015-01-28 | 浙江百山祖生物科技有限公司 | Method of detecting adulteration of ganoderma lucidum extract product by near infrared spectroscopy |
| CN105352913A (en) * | 2015-11-25 | 2016-02-24 | 浙江百山祖生物科技有限公司 | Method for detecting polysaccharide content of ganoderma lucidum extract through near-infrared spectroscopy |
| CN205120696U (en) * | 2015-11-16 | 2016-03-30 | 哈尔滨市高新技术检测服务中心 | Rice food security potential harm factor discernment model fixing device that easily happens suddenly |
| CN105527244A (en) * | 2015-10-26 | 2016-04-27 | 沈阳农业大学 | Near infrared spectrum-based Hanfu apple quality nondestructive test method |
| CN107389596A (en) * | 2017-06-26 | 2017-11-24 | 兰州大学 | A kind of method of fast prediction Barley straw trophic component |
| CN108304970A (en) * | 2018-02-05 | 2018-07-20 | 西北农林科技大学 | The method for quick predicting and system of Apple, air conditioned storage monitoring system |
| CN108535250A (en) * | 2018-04-27 | 2018-09-14 | 浙江大学 | ' Fuji ' ripe apples degree lossless detection method based on Streif indexes |
| CN109115719A (en) * | 2018-10-08 | 2019-01-01 | 华东交通大学 | A kind of Citrus Huanglongbing pathogen Band fusion rapid detection method based on high light spectrum image-forming technology |
| CN109829234A (en) * | 2019-01-30 | 2019-05-31 | 北京师范大学 | A kind of across scale Dynamic High-accuracy crop condition monitoring and yield estimation method based on high-definition remote sensing data and crop modeling |
| CN110110595A (en) * | 2019-03-28 | 2019-08-09 | 国智恒北斗好年景农业科技有限公司 | A kind of farmland portrait and medicine hypertrophy data analysing method based on satellite remote-sensing image |
| CN110320165A (en) * | 2019-08-08 | 2019-10-11 | 华南农业大学 | The Vis/NIR lossless detection method of banana soluble solid content |
| TW202041887A (en) * | 2019-04-30 | 2020-11-16 | 台灣海博特股份有限公司 | Environmental information collector system capable of figuring out the maturity of the plant growth period through optical spectrum accumulation, temperature, light intensity and humidity |
-
2021
- 2021-04-29 CN CN202110470267.7A patent/CN113155776A/en active Pending
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030129579A1 (en) * | 2001-09-04 | 2003-07-10 | Bornhop Darryl J. | Multi-use multimodal imaging chelates |
| EP1682566A2 (en) * | 2003-11-12 | 2006-07-26 | E.I. Dupont De Nemours And Company | Delta-15 desaturases suitable for altering levels of polyunsaturated fatty acids in oleaginous plants and yeast |
| CN1635202A (en) * | 2003-12-30 | 2005-07-06 | 袁兵 | Nano thermal storage warming all band infrared fibrous composition |
| JP2010025883A (en) * | 2008-07-24 | 2010-02-04 | Gifu Univ | Method of evaluating freshness of fruit and vegetable |
| WO2013148249A1 (en) * | 2012-03-27 | 2013-10-03 | Genentech, Inc. | Improved harvest operations for recombinant proteins |
| CN103278473A (en) * | 2013-05-14 | 2013-09-04 | 中国热带农业科学院分析测试中心 | Method for determining piperine and moisture content in white pepper and evaluating quality of white pepper |
| CN103971176A (en) * | 2014-05-07 | 2014-08-06 | 中国农业科学院柑桔研究所 | Method and system for optimizing harvesting decision of citrus fruits |
| CN104316489A (en) * | 2014-08-18 | 2015-01-28 | 浙江百山祖生物科技有限公司 | Method of detecting adulteration of ganoderma lucidum extract product by near infrared spectroscopy |
| CN105527244A (en) * | 2015-10-26 | 2016-04-27 | 沈阳农业大学 | Near infrared spectrum-based Hanfu apple quality nondestructive test method |
| CN205120696U (en) * | 2015-11-16 | 2016-03-30 | 哈尔滨市高新技术检测服务中心 | Rice food security potential harm factor discernment model fixing device that easily happens suddenly |
| CN105352913A (en) * | 2015-11-25 | 2016-02-24 | 浙江百山祖生物科技有限公司 | Method for detecting polysaccharide content of ganoderma lucidum extract through near-infrared spectroscopy |
| CN107389596A (en) * | 2017-06-26 | 2017-11-24 | 兰州大学 | A kind of method of fast prediction Barley straw trophic component |
| CN108304970A (en) * | 2018-02-05 | 2018-07-20 | 西北农林科技大学 | The method for quick predicting and system of Apple, air conditioned storage monitoring system |
| CN108535250A (en) * | 2018-04-27 | 2018-09-14 | 浙江大学 | ' Fuji ' ripe apples degree lossless detection method based on Streif indexes |
| CN109115719A (en) * | 2018-10-08 | 2019-01-01 | 华东交通大学 | A kind of Citrus Huanglongbing pathogen Band fusion rapid detection method based on high light spectrum image-forming technology |
| CN109829234A (en) * | 2019-01-30 | 2019-05-31 | 北京师范大学 | A kind of across scale Dynamic High-accuracy crop condition monitoring and yield estimation method based on high-definition remote sensing data and crop modeling |
| CN110110595A (en) * | 2019-03-28 | 2019-08-09 | 国智恒北斗好年景农业科技有限公司 | A kind of farmland portrait and medicine hypertrophy data analysing method based on satellite remote-sensing image |
| TW202041887A (en) * | 2019-04-30 | 2020-11-16 | 台灣海博特股份有限公司 | Environmental information collector system capable of figuring out the maturity of the plant growth period through optical spectrum accumulation, temperature, light intensity and humidity |
| CN110320165A (en) * | 2019-08-08 | 2019-10-11 | 华南农业大学 | The Vis/NIR lossless detection method of banana soluble solid content |
Non-Patent Citations (7)
| Title |
|---|
| POSTHARVEST BIOLOGY AND TECHNOLOGY ET，: "Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy", 《POSTHARVEST BIOLOGY AND TECHNOLOGY》 * |
| YANDE LIU ET，: "Visual discrimination of citrus HLB based on image features", 《VIBRATIONAL SPECTROSCOPY》 * |
| 刘燕德 等: "基于光谱指数的蜜橘成熟度评价模型研究", 《中国光学》 * |
| 唐健 等，: "江西不同生态区优质双季晚稻产量、品质及温光资源利用差异", 《扬州大学学报（农业与生命科学版)》 * |
| 陈艳玲 等，: "基于遥感信息和ＷＯＦＯＳＴ模型参数同化的冬小麦单产估算方法研究", 《麦类作物学报》 * |
| 马璞墦 等: "制浆材近红外光谱在线检测系统设计", 《测控技术》 * |
| 黄善国 等，: "《多维光网络规划与优化技术》", 30 June 2019, 北京：北京邮电大学出版社 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Neto et al. | Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves | |
| Torres et al. | Irrigation decision support based on leaf relative water content determination in olive grove using near infrared spectroscopy | |
| Matteoli et al. | A spectroscopy-based approach for automated nondestructive maturity grading of peach fruits | |
| Keskin et al. | Assessing nitrogen content of golf course turfgrass clippings using spectral reflectance | |
| Bei et al. | Leaf chlorophyll content retrieval of wheat by simulated RapidEye, Sentinel-2 and EnMAP data | |
| Wen et al. | Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red edge parameters | |
| Zhang et al. | Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels | |
| Peng et al. | Estimating total leaf chlorophyll content of gannan navel orange leaves using hyperspectral data based on partial least squares regression | |
| CN112129709A (en) | Apple tree canopy scale nitrogen content diagnosis method | |
| Asante et al. | Detection and assessment of nitrogen effect on cold tolerance for tea by hyperspectral reflectance with PLSR, PCR, and LM models | |
| Yang | Nondestructive prediction of optimal harvest time of cherry tomatoes using VIS-NIR spectroscopy and PLSR calibration | |
| CN113552096A (en) | Spectrum-based pineapple leaf nitrogen content estimation method | |
| Subedi et al. | Determination of optimum maturity stages of mangoes using fruit spectral signatures | |
| Lu et al. | Hyperspectral imaging with cost-sensitive learning for high-throughput screening of loblolly pine (pinus taeda L.) seedlings for freeze tolerance | |
| CN113155776A (en) | Prediction method for optimal harvest time of oranges | |
| Wang et al. | Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size | |
| CN115728249A (en) | Prediction method for chlorophyll content of tomato seedlings and processing terminal | |
| Martínez Vega et al. | A sampling approach for predicting the eating quality of apples using visible–near infrared spectroscopy | |
| Walsh et al. | Monitoring fruit quality and quantity in mangoes | |
| Simpson | Crop yield prediction using a CMAC neural network | |
| Rosenfeld et al. | Relationship between physical and chemical characteristics of carrots grown at northern latitudes | |
| Chen et al. | Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine | |
| CN113640230B (en) | Rapid detection method and system for field wheat moisture utilization rate | |
| CN115019205B (en) | Rape flowering phase SPAD and LAI estimation method based on unmanned aerial vehicle multispectral image | |
| CN113504186B (en) | Method for estimating utilization rate of nitrogen fertilizer in wheat by remote sensing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210723 |
|
| RJ01 | Rejection of invention patent application after publication |